Agent Awareness Modeling for Agent-Based Modeling Systems

ABSTRACT

A method for modeling agent awareness in an agent based model, the method including the steps of tracking a ratio of indicators for each agent and varying the ratio of indicators for an agent upon the occurrence of a triggering event for that agent. The method further includes using the ratio as a factor to model the agent&#39;s awareness.

This application claims priority to U.S. Provisional Patent ApplicationSer. No. 61/554,565, filed on Nov. 2, 2011, the entire contents of whichare incorporated by reference herein.

The present invention is directed to an agent-based modeling system, andmore particularly, to an awareness model for use with an agent-basedmodeling system.

BACKGROUND

Agent-based systems and models may be used to simulate human or otherbehavior in a wide variety of settings and fields, such as purchasingactivity, social interaction, traffic flow, logistics, biomedical,portfolio management, population dynamics, combat behavior and others.Such agent-based systems typically must first create a set of agents andassign a set of characteristics and/or attributes to the agent.

In many purchase forecast agent-based systems, agents are assigned anawareness attribute. However, many existing purchase forecastagent-based systems treat awareness as a binary on/off characteristicwhich operates as a mere threshold to certain activity, and does notprovide an accurate model of awareness in the real world.

SUMMARY

In one embodiment, the present invention is a method for modeling agentawareness in an agent based model. The method includes the steps oftracking a ratio of indicators for each agent and varying the ratio ofindicators for an agent upon the occurrence of a triggering event forthat agent. The method further includes using the ratio as a factor tomodel the agent's awareness.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a sample graph showing a probability density function of agefor a sample population;

FIG. 2 is a sample graph showing a cumulative distribution function ofage for a sample population;

FIG. 3 is a sample graph showing a cumulative distribution function ofincome for a sample population;

FIG. 4 is a sample chart showing results under a first method forselecting a correlated agent variable (income) based upon anothervariable (age);

FIG. 5 is a sample chart showing results under a second method forselecting a correlated agent variable (income) based upon anothervariable (age);

FIG. 6 is a sample chart showing results under a third method forselecting a correlated agent variable (income) based upon anothervariable (age);

FIG. 7 is a sample chart showing results under a fourth method forselecting a correlated agent variable (income) based upon anothervariable (age);

FIG. 8A-8C illustrate a series of steps showing an awareness model;

FIG. 9 is a graph illustrating actual sales data compared to estimatedsales, over time, for a particular simulation;

FIGS. 10 and 11 graphically illustrate certain steps of a method foroptimization of a variable; and

FIG. 12 is a graph illustrating the relationship between error (MAPE)and a particular variable.

DETAILED DESCRIPTION

1. Overview

The present invention takes the form of, or may be used in conjunctionwith, an agent-based modeling system, alternately termed an agent-basedmodel (“ABM”). In such a system, a number of agents are created andvarious attributes/characteristics are assigned to each agent. Eachagent can correspond to a decision-making entity, such as an individualperson, organization, or organism in one case. Behavioral rules andconstraints regarding the agents' behavior and interaction are thencreated and applied, as appropriate, to match the simulation/environmentat issue. The ABM is then calibrated using test-run simulations orcalibration simulations to ensure that the model, agent characteristicsand other assumptions are appropriate and lead to accurate outcomes ascompared to historical data. After the model and other underlyingassumptions/characteristics are sufficiently calibrated and validated,the final or “actual” simulation is run. During a calibration or actualsimulation, each agent can make decisions and react to input/stimuli onan individual basis, based upon the input/stimuli and the agent's owncharacteristics and constraints. The resultant behavior of each agent istracked and recorded. Once the simulation is concluded, the outcomes ofeach agent, and the system as a whole, can be processed, analyzed andreviewed to extract appropriate data.

In one particular embodiment described herein, the agent-based system orABM is used to determine consumer behavior (particularly purchasingbehavior) in response to particular marketing activities. Accordingly,this particular example is described in some detail herein to illustratethe various principles of the invention(s). However, it should beunderstood that the systems and methods described herein are notnecessarily limited to such purchasing/marketing simulations, and thesystems and methods described herein can be used in any of a widevariety of agent-based systems or ABMs.

2. Agent Generation

As outlined above, a preliminary step in creating and utilizing theagent-based system involves the creation of the agents. The number ofagents will vary depending upon the granularity of the desired results,time and cost limitations, computing power limitations, the type andnature of simulation, etc. In one case, however, the number of agentsranges between about 20,000 and about 1,000,000. Each agent may or maynot be assigned a unique identifier, after which the agent is assignedvarious attributes, characteristics, qualities and the like(collectively termed attributes, or parameters, or variables herein).The type and number of attributes assigned to an agent will varydepending upon the nature of the simulation and the agent-based system.In cases for systems used for tracking or predicting purchase behaviorand/or the effectiveness of advertising, the attributes can include sex,age, race, income, TV watching habits, Internet usage habits, radiolistening habits, price sensitivity, historical purchase occasions,quality sensitivity, etc. The number of attributes can vary as desired,in one case the number of attributes is between about five and aboutfifty.

In order to assign attributes to the agents, various data sources may bereferenced as a starting point for certain characteristics, such as age,race, income, geographic location, etc. For example, census data, datafrom marketing research organizations and information-gatheringorganizations, information from a particular client or customer for whomthe simulation will be performed, or other sources, may be referenced.The population data may then, in some cases, be culled as appropriate,by, for example, limiting populations to those geographic areas where aproduct is available for purchase, or limiting population by legal agewhere the simulation involves the purchase of alcoholic products, etc.

Next, correlations between the attributes, and a natural order to thecorrelated variables, may be assigned. For example, a person's incomemay be assumed for the purpose of the simulation/system to be correlatedto his or her race and gender. However, a real consumer's race andgender were “assigned” before his or her income. In this case, if inreality there is a causal direction, race and/or gender may beconsidered causal or predictive attributes or values, and income may beconsidered a resultant attribute or value, as an example.

Based upon the available, pertinent data, a probability density function(“PDF”) for an attribute, such as age, may be generated or obtained.FIG. 1 illustrates an example of a probability density function for ageas a percent of population. Thus, the graph of FIG. 1 shows what percentof the population (on the vertical axis) has a given age (along thehorizontal axis). In order to facilitate use with the current system, aprobability density function, such as that shown in FIG. 1, is typicallyconverted to a cumulative distribution function, such as that shown inFIG. 2. Thus, the cumulative distribution function G(a) of FIG. 2 showswhat percent, or what ratio, of the population (along the vertical axis)has at most a given age (along the horizontal axis). Thus, in theexample marked on FIG. 2, 0.6 ratio of the population (i.e. 60%) has anage of at most about 40. The function G(a) of FIG. 2 illustrates auniformly distributed variable/attribute in that it is normalizedbetween 0 and 1 on the vertical axis, and all values on the verticalaxis are equally selectable.

Once a cumulative distribution function for a particular attribute isgenerated/obtained (such as that shown in FIG. 2), the cumulativedistribution function can be disaggregated and broken down such thatparticular ages can be assigned to particular agents. In order to dothis, a random number (between 0 and 1) is sampled from a uniformdistribution. The inverse of the sampled random number in the cumulativedistribution function for age (G(a)) is then calculated. For example, ifthe random number generator generates a number of 0.6 (the random draw),the cumulative distribution function of FIG. 2 determines that theinverse of the 0.6 value is age 40, which is then assigned to aparticular agent (it is noted that very rough numbers are used hereinfor the draw, age, and other data for ease of illustration, and inactual use the data will typically have greater precision).

The process can then be repeated for each agent, generating a separaterandom number draw for each agent until the population of agents havetheir assigned ages. In this manner, the age assignment process can bedescribed as taking the inverse G⁻¹ (a) of a collection of random drawson a uniform distribution. This system and methodology can also beapplied to attributes which have discrete values (such as gender andrace) instead of more continuously variable values such as income, usingwell-known techniques involving step-function forms for the inverses ofthe cumulative distribution function graphs.

If desired, this process of taking the inverse of a uniform distributioncan be repeated for other variables/attributes besides age, such asgender, race and the other variables/attributes outlined above. In somesystems, independent uniform random draws utilized are for each of thevariables/attributes of a single agent (i.e. a new random number isgenerated for each attribute). In this case, however, there will nottypically be any correlation between the variables/attributes, whichresults in agents having unrealistic characteristics.

For example, in one case, in other systems a single uniform random drawis utilized across the board for all of the agent'svariables/attributes. For example, in such a case the 0.6 number in theexample above used to determine the agent's age is also is used todetermine the agents' gender, race, income and other attributes byapplying (i.e. inverting) the 0.6 number to/in cumulative distributionfunctions for those attributes. In other words, one uniform draw isutilized as an input to generate all or several attributes for theagent. In this case, as shown in FIG. 3, an application of the 0.6random draw results in an income of $58,000.

However, this approach of using the same uniform random draw fails tocreate an accurate model of real-world conditions. In particular, insuch an approach, the age of the agent essentially acts as the soledeterminer of the agent's income and other attributes, such that in theexample set forth above the agent would be in the “top 60%” for allattributes; and in this example each 40 year would have an income of$58,000. However, this is of course not an accurate model of thediversity of characteristics present in the real world. While thisuniform approach ensures a correlation between related variables (i.e.age and income), the correlation is too strong and does not sufficientlytake into account the variability of attributes of real worldindividuals.

Accordingly, in order to provide more accurate attributes to the agents,the input values for determining one of the agent's attributes (i.e.age) can be blended with a random number draw (i.e. unrelated to age orother sampled variables) to create a new set of input values fordetermining a correlated attribute (i.e. income). This system enablesthe resultant agent population to have more realistic, diverseattributes with a correlation (but not a strict correlation) between theattributes. For example, the chart shown in FIG. 4 illustrates age andincome attributes of various agents (it should be understood, of course,that the number of agents in an actual simulation can significantlyexceed the number shown in FIG. 4). The first column of the chartillustrates the age draw u₁ for the agents, beginning with the exampleof 0.6, as the uniform age draw, as addressed above. As can be seen inthe chart, the age draw of 0.6, when applied as an input/inverted in theage cumulative distribution function (such as that of FIG. 2), providesan age value of 40. When the draw of 0.6 is applied as an input/inverseto the income cumulative distribution function (such as that of FIG. 3),an income of $58,000 results. Various other outcomes can be seen in thechart of FIG. 4, in which it can be seen that income and age have astrict correlation.

In contrast, the chart of FIG. 5 illustrates the system outlined abovein which blended draws are utilized. In the chart of FIG. 5, the agedraw and inverse functions in the first two columns remain the same asthose in FIG. 4. However, the third column of the chart of FIG. 5illustrates another uniform random draw u₂, which is then blended withthe age draws of the first column u₁, resulting in the blended variableg(u₁, u₂) of the fourth column. Finally, the fifth column of the tableof FIG. 5 illustrates the income attribute which is ultimately assignedto the agent based upon the input/inverse of the fourth column.

Both the age draw u₁ and the uniform random draw u₂ areuniformly-distributed variables. In order to form the blended variableg(u₁, u₂) in the fourth column, the uniformly distributed variables u₁,u₂ must be blended together in such a way to result in anotheruniformly-distributed variable g(u₁, u₂) that is related to, and hassome correlation to, both of the input variables u₁, u₂. Thus, in orderto blend the uniformly-distributed variables u₁, u₂, the followingmethodology may be used.

Since u₁ and u₂ are uniformly distributed random variables, they can beblended to form a blended uniformly distributed variable g(u₁, u₂). Inone case, the following equation can be used:

$\begin{matrix}{{g\left( {u_{1},u_{2}} \right)} = \begin{Bmatrix}{{{\frac{\left( {u_{1} + u_{2}} \right)^{2}}{2}\mspace{14mu} {if}\mspace{14mu} u_{1}} + u_{2}} < 1} \\{\frac{\left( {2 - \left( {2 - u_{1} - u_{2}} \right)^{2}} \right.}{2}\mspace{14mu} {otherwise}}\end{Bmatrix}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

The resultant function {g(u₁, u₂); u₁, u₂ uniform} generated by usingEquation 1 is uniformly distributed, as can be seen by the followingproof In particular, it can be shown that the cumulative distributionfunction of the above set given u₁+u₂<1 is the identity, which impliesthat the probability density function is a constant (one). We thenprovide a transformation of the alternate case that shows that the sameproof can be applied.

Suppose that 0<F<½. It can be easily shown that g(u₁,u₂)<F<½ only ifu₁+u₂<1 so without loss of generality, g(u₁, u₂)<F if and only if0<u₁<√{square root over (2F)} and 0<u₂<√{square root over (2F)}−u₁.Because the probability density function of u₁ and u₂ are bothidentically one,

Prob(g(u ₁ ,u ₂)<F)=∫₀ ^(√{square root over (2F)})(∫₀^(√{square root over (2F)}−u) ¹ du ₂)du ₁

So

$\begin{matrix}{{{Prob}\left( {{g\left( {u_{1},u_{2}} \right)} < F} \right)} = {\int_{0}^{\sqrt{2F}}{\left\lbrack u_{2} \right\rbrack_{0}^{\sqrt{2F} - u_{1}}{u_{1}}}}} \\{= {\int_{0}^{\sqrt{2F}}{\left( {\sqrt{2F} - u_{1}} \right){u_{1}}}}} \\{= \left\lbrack {{\sqrt{2F}u_{1}} - \frac{u_{1}^{2}}{2}} \right\rbrack_{0}^{\sqrt{2F}}} \\{= {{{2F} - {\frac{1}{2}\left( {2F} \right)}} = F}}\end{matrix}$

It remains to be shown that if ½<=F<1 then

Prob(g(u ₁ ,u ₂)<F)=Prob(g(u ₁ , u ₂)<½)+Prob(½<g(u ₁ , u ₂)<F)=F

This is equivalent to showing that Prob(½<g(u₁, u₂)<F)=F−½. We know thatwe are using the second range in our functional definition, so g fallsin this range if and only if

$\frac{1}{2} < \frac{\left( {2 - \left( {2 - u_{1} - u_{2}} \right)^{2}} \right.}{2} < F$

A simple translation to

v ₁=1−u ₁

v ₂=1−u ₂

G=1−F

Shows us that g is in the range if and only if

${\frac{1}{2} < \frac{\left( {2 - \left( {v_{1} + v_{2}} \right)^{2}} \right)}{2} < {1 - G}},$

if and only if

$G < \frac{\left( {v_{1} + v_{2}} \right)^{2}}{2} < {1/2}$

This puts us back in the space of the original proof. We subtract theprobability below G and get

Prob(G<g(u ₁ , u ₂)<½)=Prob(g(u ₁ , u ₂)<½)−Prob(g(u ₁ , u₂)<G)=(½)−Prob(g(u ₁ , u ₂)<G)=½−G=F−½

Thus, the function outlined above (Equation 1) may be used to blend thevariables in the manner outlined above, resulting in a uniformly blendedoutput; i.e. in which a histogram of the outputs is between 0 and 1,meaning all values in that interval are generally equally represented.If the relationship between the prior or causal variable and theresultant variable is negative, this can be accommodated by transformingthe uniform draw for the prior variable, U, to 1−U before blending. Thissystem and method for blending variables avoids having to utilize jointdistributions, which requires unknown data, and avoids having to know orestimate conditional probabilities that are required in order to carryout Gibbs' sampling.

In addition, the system outlined above can be modified such that thesystem is assigned attributes in a related manner to drive or dilute thedesired correlation in the end result. For example, rather than simplyblending the age draw with a random draw to arrive at the blendednumber, that blended number (i.e. an intermediate value) may then, inturn, be blended with the age draw, as shown in the table of FIG. 6. Thefirst four columns of FIG. 6 are the same as those of FIG. 5. However,as shown in the fifth column of the table of FIG. 6, the draws of thefirst and fourth columns are be “re-blended.” The resultant re-blendeddraw in the fifth column of FIG. 6 g(g(u₁, u₂), u₁) (i.e. an end value)is more closely (but not entirely) correlated with the uniform age drawthan the blended draw g(u₁, u₂) of FIG. 5. This means that the resultantincome assigned to that agent is more closely (but not entirely)correlated with age.

In order to blend the blended draw g(u₁, u₂) with the age draw (u₁) toprovide the re-blended draw, the same function g outlined above inEquation 1 may be used to again blend the desired functions, resultingin the function g(g(u₁, u₂), u₁). The result of such a re-blending isshown in the table of FIG. 6. This resultant outcome (income) is on theaverage more closely tied to age than the table of FIG. 5, but also doesnot have the strict correlation of the table of FIG. 4. If desired, there-blended variable g(g(u₁, u₂), u₁) can be again blended with the agedraw u₁, or with other draws, to provide the desired correlation betweenthe two or more attributes.

Alternately, if the attribute/variable is desired to be diluted, anotherrandom draw u₃ may be blended with the blended draw g(u₁, u₂), as shownin the table of FIG. 7. In this case, the resultant outcome (income) ison the average less closely correlated with age than the table of FIG.5. In this manner, the system allows variables to be sampled withcorrelation to the previously-sampled variables with the desiredstrength of relation/desired randomness to suit the desires of thedesigner/user of the system. In contrast, a strict, one-step blendingdoes not provide flexibility to the designer to allow the draw to bestructured as desired.

Thus, broadly speaking, the system involves, in one case, threesteps: 1) choosing an order to the variables; 2) sampling the “early” or“causal” attributes in the standard manner outlined above; that is,obtaining a cumulative distribution function and sampling a random,uniformly-distributed variable, and taking the inverse of draws of thatvariable in that function; and 3) for any variable that is correlated toone or more of the earlier or causal variables, blending the value ofthe random draw variable used to sample the earlier/causal variable withanother truly random uniform variable. The resultantuniformly-distributed variable is then blended with others until avariable that is uniformly distributed, but correlated topreviously-sampled variable(s), results. That blended,uniformly-distributed variable is then used to sample the correlatedvariable.

The blending process can be done repeatedly, in as many combination asdesired, although it is not possible to be done fractionally. Forexample, if the relationship between two variables/attributes is desiredto be relatively strong, the input value that was used to sample thefirst variable could be re-blended, with the blended variable, as manytimes as desired. On the other hand, if it is desired to make thevariables/attributes more independent, the resultant, blended variablecould be sampled with a purely random, uniformly-distributed variable asmany times as desired. Multiple variables/attributes, and multiplerandom number draws, may be blended as desired. The resultant systemintroduces some randomness/variability into the system, in the desiredmanner, such that agents having more accurate, real-world attributes areprovided, which can result in more accurate simulations.

3. Awareness Model

After the agents are created, and their attributes assigned, in manycases an agent is assigned an awareness attribute or value which istaken into consideration during the simulation. For example, inpurchasing or marketplace agent-based models, the agent must first beknown to be aware of a product or brand before he or she can purchasethe particular product or brand. Thus, in many existing models, theagent's awareness is treated as a binary on/off characteristic. Anagent's lack of awareness acts as a barrier to purchase, and converselyand agent's awareness of the brand/product means the agent is eligibleto purchase the brand/product.

The ABM system described herein may utilize an Awareness Frequency Ratio(“AFR”) which represents the percentage of stimuli or other cues whichthe agent encounters which cause the agent to think about a given brand.The higher the AFR for an agent, the more prone the agent is to purchasethe particular associated good during a purchase opportunity. Thus, insuch a model, it may be a primary goal of advertising to generate asmany connections as possible, thereby increasing the AFR the agents.Each agent also has a purchase probability (“PP”) that is independent ofthe agent's awareness or AFR. The purchase probability may represent theagent's propensity to buy the particular item when the agent is“perfectly” aware of the item and in the position to purchase (e.g. at astore or on a purchase-enabled website), and each agent's PP may beindependent of other agents' PP.

If the agent is unaware of the particular item (i.e. AFR is zero), theagent is precluded from purchasing the item, no matter how much theagent might be likely to buy the item if the agent were aware. Thus,purchase probability and AFR work together in the simulation to predictagent purchasing behavior. In one embodiment, the present system modelsawareness, or AFR, as a dynamic characteristic which varies over time,and can change based upon each individual agent's experiences andattributes.

In one embodiment, the present system models awareness based upon aprocess that can be considered similar to a Polya Urn model, but withcertain variations. The Polya Urn model can be visualized in consideringan “urn” or container that stores a predetermined number of items, suchas balls, therein. Various types of balls (such as black and white, inone case) are stored in the urn. In this scenario, the black balls arereminders of a given brand, and the white balls are reminders of acompetitor.

When there is a triggering event, the model simulates the randomselection of a ball from the urn. Once a ball has been selected, it isidentified (i.e., white or black). The original selected ball is thenplaced in the urn, along with another ball of the same color. Theprocess of selecting a ball, and replacing the ball along with anotherof the same color, is repeated for a number of trials. As the number oftrials approaches infinity, the proportion of ball in the urn reaches abeta distribution across the population of agents.

The present system models agent's awareness using the self-reinforcingprinciples of Polya Urn, but it is distinctly different from the PolyaUrn model. For example, as shown in FIG. 8A, each agent may have anassociated virtual urn or container 10 which stores a number of virtualindicators, such as black balls 12 and white balls 14 therein. As in theexample set forth above, the black balls 12 are reminders of a givenbrand, and the white balls 14 are reminders of another brand. The numberand ratio of balls 12, 14 in the urn 10 at the beginning of thesimulation may represent the number of recent previous mental remindersor brand associations for an agent. Alternately, if desired, the urn 10may not have any balls 12, 14 therein at the start of the simulation.

When the agent experiences a triggering event, a certain number of balls14, 16 are added to the urn 10. The triggering event can take any widevariety of forms. In one case, however, the triggering event takes theform of a purchase activity by the agent, or a marketing experience bythe agent (i.e. viewing or experiencing a marketing message), or adistribution event (i.e. viewing the product in a format available forpurchase; such as sitting on a shelf at a store or available for on-linepurchase). The number of balls to be added to each agent's urn for atriggering event can vary, and may be selected/optimized to provide themost accurate results, such as by the optimization process described inthe next section. In one embodiment, each agent in the simulationreceives the same number of balls for each triggering event.

In one embodiment, for example, when an agent views a televisionadvertisement for the white product, three white balls 14 are added toeach agent's urn, as shown in FIG. 8B. When an agent views anadvertisement for the black product when visiting a web site, five blackballs 12 are added to each agent's urn 10, as shown in FIG. 8C. In thisexample, then, the agent may be more sensitive to internet advertisingthan television advertising, and this is reflected in the number ofballs added to the agents' urns.

As noted above, various parameters of the model can be modified asdesired. For example, the number of starting balls (if any) in the urn,the number of types of balls, how many balls are added upon a triggeringevent, what constitutes a triggering activity, and other parameters maybe varied. Each agent can have more or less sensitivity to advertising(or differing types of advertising), or to purchases activities (ordiffering types of purchase activities), or to distributions (or todiffering types of distribution). Moreover, rather than adding ballsupon a triggering event, balls could instead or in addition be removedto arrive at the desired ratio of balls.

In addition, the awareness model may have a “decay rate” such thatrandomly-selected balls or portions thereof are removed from the urn 10at predetermined intervals. This decay rate is selected to modelreal-world conditions wherein marketing experiences can be forgotten orreplaced over time. In this case, then, the passage of time (i.e.simulated passage of time in the model) can be considered a triggeringevent, causing balls to be removed.

This system/model can be seen to accurately model human behavior, and isself-reinforcing in that the purchase triggering activities add moreballs of the appropriate color to the agent's urn, and thereby makesubsequent purchases more likely.

The AFR for each agent can be calculated by taking the ratio of balls ofdiffering types/colors at any one time. Thus, awareness can beconsidered to be modeled based upon the frequency of remindersexperienced by each agent, and the agent's receptiveness to the type ofreminders. The AFR for each agent can then be utilized in simulations todetermine purchase behavior, i.e. considering both agent preference andawareness. For example, in a simplistic model an agent's AFR ismultiplied by the agent's PP, when the agent is in a position to make apurchase, to determine whether the agent makes the purchase.

4. Auto Calibration

Various parameters of the system may need to be calibrated to theappropriate values before the simulation is run. In many cases, theowner/operator/model builder of the ABM system is provided withhistorical data. For example, in the marketing/sales ABM exampleaddressed herein, the system operator may be provided with historicalsales data for a particular product (or class of products) of interest.The operator of the system may also be provided with various historicalinputs, such as marketing efforts, including types of marketing efforts,market reach of the marketing efforts, duration of the marketingefforts, etc. associated with the historical sales data. In this mannerbefore the ABM system is operated to simulate projected or future sales,the system can first be run upon known inputs, and compared to knownoutputs (i.e. historical sales data) to determine the accuracy of themodel. The model parameters can then be adjusted in an appropriatemanner until the model provides an accurate output, in a calibrationprocess.

FIG. 9 illustrates a graph showing actual sales of a particular product,or class of products, over time. The graph of FIG. 9 also includesforecast/projected sales data based upon a simulation using the ABMsystem. As can be seen, the forecast output differs from the actualsales data. The auto calibration system/algorithm outlined herein can beutilized to automatically calibrate the system variables/parameters toreduce or minimize the error between forecast/projected sales and actualsales. Once the error is minimized in the auto calibration process, thesimulation can be run for future/projected sales data based upon theparameters and inputs which supply minimal error in the calibration.

As shown in the graph of FIG. 9, the forecast/modeled/estimated salesdata will differ from the actual sales data, typically underestimatingand overestimating sales at differing times. Each such projection, suchas the one shown in the graph in FIG. 9, provides a total errormeasurement known as Mean Absolute Percentage Error (“MAPE”). MAPErepresents the average absolute percent error between the actual dataand forecast data. The “Error” in the Mean Absolute Percentage Error canbe roughly visualized as the distance between the forecast and actualsales graphs at particular intervals (e.g., weekly intervals in onecase). In the example of FIG. 9, the MAPE is identified as 7.2%. Ingeneral, the auto calibration system may seek to minimize MAPE,although, as will be described in greater detail below, the system mayalso seek to optimize certain other features.

Broadly speaking, in order to minimize MAPE (or some other value, whichmay be desired to be optimized/minimized), a simulation under a set ofcircumstances for a set period of time is run, and the MAPE value forthe simulation is calculated and noted. One or more parameters are thenvaried, and the simulation is run again with the variedparameters/inputs. The MAPE of the resultant, second run is thenreviewed. The MAPE from the first run and the second run are comparedand utilized to project the next change to be instituted to the variableor variables of interest. In one embodiment, as will be described ingreater detail below, the system uses a modified version of Newton'sMethod to calculate the next value to be tried for the variable. Thesesteps are repeated until a local or inferred curve or graph of MAPE withrespect to a particular variable or variables is generated/projected,and/or the zero or minimum error of the MAPE curve with respect to thevariable(s) are sufficiently identified or estimated, or the maximumnumber of iterations is reached.

For example, with reference to FIG. 10, the line 20 plotted therein(g(P)) represents the values of MAPE of simulations carried under acertain set of parameters and assumptions, wherein one of the variablesis modified. The slope of the tangent line 22 at a particular point (theend or adjacent to the end, in the illustrated embodiment) of the line20 can be considered to represent the (partial) derivative of MAPE withrespect to the variable of interest. The tangent line itself is the bestlocal estimate for the error as a function of this parameter. Forexample, in the particular illustrative case shown in FIG. 10, thevariable of interest (the variable being modified along the x axis) isthe number of balls added to the awareness model urn for a certaintriggering event, such as exposure to watching a televisionadvertisement.

When approximating the zeros of a differentiable function, Newton'sMethod directs one to begin at a point, evaluate the function andcalculate its slope/derivative and (assuming the derivative is non-zero)move to the X-value where the intercept would be if the slope at thatpoint were to be held constant. The X-value at that intercept point canthen be used as the next value in the calibration process. Thus,according to Newton's Method, one manner in which error/MAPE for a givenvariable can be projected to be minimized is to estimate the derivativeor instantaneous/local slope (in a manner described in greater detailbelow) at a point (say, point P₁) along the graph g(P) 20, shown as theslope of tangent line 22. The point P₁ can be the most recent pointcalculated by a simulation, or the point closest to zero for the valueof MAPE, or selected otherwise. Once point P₁ is selected, the slope oftangent line 22 of the line 20 at point P₁ is then calculated andextrapolated until it intersects the X axis (at point P₂ of FIG. 10; thepoint of zero error/MAPE). Thus, in one case, the value for theparameter to be utilized during the next simulation is determined by theequation:

P ₂ =P ₁ −g(P ₁)/g′ (P ₁)   (Equation 2)

In the example of FIG. 10, then, the value of the variable v used in thenext simulation would be the intercept of the slope line 22 with the Xaxis at point P₂; that is, five balls in the illustrated embodiment.

It is difficult if not impossible for any agent model to fully satisfythe assumptions of Newton's Method. Since the “function” g(P) is not aclosed form function, which means the function g(P) cannot be accuratelymodeled by a mathematical equation, not to speak of differentiable one.However, the error in the agent model in question is continuous (changesgradually) with respect to its parameters, so this algorithm can benefitfrom the principles of Newton's Method by estimating the derivativeinstead of calculating the derivative. Therefore the local slope of theerror curve at point P₁ may be estimated by obtaining another projecteddata point on the curve 20 and calculating the slope of the errorfunction between the new, projected point and the point P₁.

In particular, in order to calculate the slope of tangent line 22 atpoint P₁ the simulation may be rerun using the same parameters used togenerate point P₁, except the variable v of interest is increased (ordecreased) slightly, while all other variables are held constant (or, ifmodified, done so in only minor manners). In other words the simulationis rerun using (P₁+Δ P) in place of P₁, and the resultant output oferror (MAPE) is plotted. Point 24 in FIG. 10 represents the MAPE valuewhen the simulation is run using (P₁+Δ P). The dotted line projection ofcurve 20 between point P₁ and point 24 (P₁+Δ P) can then be used tocalculate the slope 22 of line 20 at point P₁. In particular the localslope 22 can be estimated as:

g′(P ₁)≈(g(P ₁ +Δ P)−g(P ₁))/Δ P   (Equation 3)

Thus, once the slope g′(P₁) (or δg/δP₁ in the case of a partialderivative) is estimated according to Equation 3, the slope value forg′(P₁) can be used in Equation 2 to calculate the next value of thevariable v to be used in the next “full-run” or final simulation to seekto minimize MAPE for the system as a whole (e.g. wherein the variable ofinterest is five balls in the example of FIG. 10).

The process of taking the value for a variable v, incrementallyadjusting the value, calculating an error value, using that error valueto estimate the local slope of the variable, and projecting the localslope to determine the next projected value for the variable to be runfor the next simulation can then be repeated for each of the variables(e.g. five time, fifty times, or whatever is necessary to match thenumber of variables). Once the “next step” for each variable iscalculated, a full-run simulation is carried out using the new (Newton'sestimated) values for all variables. The resultant MAPE is then trackedand used as a basis for further calibration, as desired. This process ofgenerating a projected values for each variable, running a full runsimulation, and examining MAPE can then be repeated as many times asdesired.

FIG. 11 illustrates the case where many more data points for the linefunction g(P), compared to FIG. 10, have been generated using thisalgorithm. The algorithm may then be repeated until error/MAPE reaches aminimum and/or begins to increase. In other words, the methoditeratively calculates MAPE using the above recursive sequence until thevalues converge to a minimum (if they do in fact converge), or until apredetermined number of iterations is reached. As shown in FIG. 11,after the auto-calibration process is complete, a locally defined curveshowing values of error/MAPE for various values of thevariable/parameter may be inferred in which it can be seen that one ormore particular values for the parameter provide a minimum, oracceptable error. For example, as shown in FIG. 11, the value for MAPEis minimized when the variable v is about 6.5 balls, which could then betaken as the optimal value once the optimization process is complete.

The system/algorithm outlined above generally follows Newton's Methodfor finding minimum values of functions. However, the system/algorithmslightly differs from Newton's Method in that Newton's Method is, intheory, only valid for use in closed-form, differentiable functions,whereas the function/curve for MAPE/errors will generally not be in aclosed form. This system thus can also be thought of as anextrapolative, multi-dimensional, local-to-the-current-parameter-setapplication of the intercept method. Thus, using this system, estimatesof partial derivatives applied to every variable can be estimated, whichprovide an intelligent trajectory towards the minimal error scenario.

As noted above, Equation 2, derived from Newton's Method, can beutilized to calculate the next value for the parameter to be utilized inthe next simulation. Equation 2 provides a “full step” for determiningthe next value of the parameter. However, in some cases, it may bedesired to take less than the “full step” as the next value for theparameter (i.e. moving from four balls to five balls in the example ofFIG. 10). In particular, as shown in FIG. 12, in some cases the MAPEcurve can have various minimums, and a minimum (such as minimum 24) canbe overstepped if a full step (illustrated by the angled line 26 of FIG.12) is utilized. Accordingly, in order to minimize the possibility ofoverstepping minima of the MAPE curve, some fraction of the full stepprovided by Newton's Method/Equation 2 may be implemented. Even partialsteps can be sufficient to cause significant changes in MAPE.

When this “partial step” methodology is utilized, various techniques canbe implemented for determining how large a portion of the full step isactually implemented. For example, when the parameter of interest is amarketing parameter, the system may apply a ratio of the marketingparameter, as compared to the total number of marketing parameters ofthe system, to the projected step. By way of example, if there are sixmarketing parameters under consideration by the system, then the systemmay implement ⅙ of a full step projected under the method outlinedabove. The fraction or percentage of the adjustment step can also beadjusted by various other methods and can be determined by various otherratios such as, for example, the number of modeled items in a forecast,or by considering other ratios. When PPP is being optimized under theauto-calibration process, a truncated step may also be similarlyapplied. In other cases, the full step projected by Newton's Method, ormultipliers of that step (i.e. multiplier of less than one, or even morethan one) can be implemented.

In some cases, other factors, besides MAPE, may be taken intoconsideration before the projected adjustment to a parameter isaccepted. In other words, if a proposed adjustment to a parameter woulddecrease certain other goals of the system, then the proposed adjustmentto a variable may be rejected, regardless of the effect upon MAPE. Sucha concept is termed “selfishness” herein on the basis that theoptimization should ultimately be carried out in a manner which seeks tomeet the overall goals of the system designer.

In some cases, the characteristics of certain variables are generallydesired to be driven in a certain manner, regardless of the effect uponoverall MAPE. For example, when the system is optimizing values relatingto customer awareness of a brand, it may be known or assumed that incertain cases (i.e. in the case of a mature brand) customer awarenessover time remains generally constant (i.e. over the course of thesimulation). In this case, when values which affect awareness are beingoptimized, rather than seeking to minimize MAPE, the method describedabove can be utilized to ensure awareness remains constant.

For example, the graphs of FIGS. 10-12, illustrating various values ofMAPE as function of a particular variable, could instead map the valuesfor MAPE added to the absolute value of average slope of changes inawareness. In other words, parameters affecting awareness may bemodified such that the average value of the slope of the “awarenessfrequency curve” over time is minimized. Alternately, rather than simplyminimizing MAPE and possibly seeking to keep awareness constant,combinations of these factors can be considered in seeking theappropriate parameter values. In this case, or further alternately, thesystem can seek to minimize “total error” which can be defined as acombination of the change of awareness over time (or some other variableof interest) and MAPE. Further alternately, the principles ofselfishness may force short term increases in MAPE since the long termgoal of the system is to find a global minimum for MAPE. The system maythus know that a temporarily/local increase of MAPE is in many casesnecessary in order to find a global minimum for MAPE. Thus thesimulations can be carried out in a manner which actually increase MAPE,so long as the other (long term) goals are being reached.

The following illustrative parameters for use in tracking purchasingbehavior, among others, can be calibrated during the calibrationperiod: 1) the overall reach of competitive media; 2) the impact ofseeing a product on the shelf at a store or at one's home; 3) the speedat which advertising or other impressions are forgotten; 4) the strengthof various types of media (in terms of its ability to change purchaseprobabilities); 5) the mean of the underlying distribution of preferencefor a brand; 6) the cause of sales trends for particular brands; and 7)price elasticity of a promotion. However, the number and type ofparameters to be optimized can vary widely depending upon the nature ofthe simulation, the ABM system, the outputs desired, etc.

Moreover, not all variables/parameters of the system may necessarilyneed to be optimized. In particular, it may be determined that changesin particular parameters/variables may affect the ultimate results/data(e.g. sales) so insignificantly such that it is not worth the resourcesto carry out the optimization process for those variables. It may alsobe desired to first optimize parameters which are known, or suspected,to introduce the most variability into the system.

For example, in the marketing context, the system may first run a seriesof test simulations in which each media-based variable or parameter isadjusted by the same incremental or percentage amount ΔP, while allother variables remain constant. The results of the test simulations,and in particular magnitude of the change in MAPE or other outcome, arethen examined. Those variable which cause a sufficient change in MAPE oroutcome may be considered for further optimization, while those that donot cause a sufficient change in MAPE or outcome may be dropped from theoptimization process. In one case, a variable may proceed to theoptimization process only if the incremental adjustment ΔP changes theMAPE or outcome of the simulation by a value that exceeds the error forthe associated simulation.

The system may also be configured that variables are optimized in order.In other words, those variables that cause the largest change in MAPE oroutcome during the test simulations can be optimized first. In thismanner the system first focuses upon what is expected to be the moresignificant parameters (i.e. the “grosser” parameters), and thenprogressively optimizes the “finer” parameters.

In one embodiment, only system-wide parameters, such as PPP or otherparameters which apply equally to each agent, may be optimized usingthis system. However, it may also be possible to use this system tooptimize parameters which are agent-specific, including variablesrelating to the Polya Urn model, AFR, PP, and others.

As noted above, this optimization process can be carried outautomatically by computer, processor, or the like without humanintervention. This represents a significant improvement over manyexisting methods in which the operator would run a simulation, reviewthe results, and manually adjust one or more variables. The simulationwould then be run again, and the results reviewed. This is a highlylabor-intensive, time-consuming and repetitive process. In contrast, thepresent auto-calibration system enables the system to calibrateparameters automatically, which can then be presented to an operator forconfirmation and/or manual adjustment, if desired.

Due to the high volume of data needed to be tracked and calculated, thesystem can be computer-implemented. For example, a computer can be usedto set up and/or administer the program, and carry out the steps,processes and methodologies outlined above, including creating storingand tracking the identity of and information relating to the agents,their parameters, MAPE, optimization steps and parameters, awarenessmodels, etc. As used herein “computer” means computers, laptopcomputers, computer components and elements of a computer, such ashardware, firmware, virtualized hardware and firmware, combinationsthereof, tablet computers, mobile devices, smart phones, or software inexecution. One or more computers can reside in or on a server in variousembodiments and the server can itself be comprised of multiplecomputers. One or more computers can reside within a process and/orthread of execution, and a computer can be localized at one locationand/or distributed between two or more locations.

The computer can include a memory, a processor, and a user interface(which can include, for example, a keyboard, mouse or other cursorcontrol device, other input devices, screen/monitor, printer, otheroutput device etc.) to receive inputs from, and provide outputs to, auser. The computer can be operatively coupled to a database which canstore information relating to the agents, their parameters, MAPE,optimization steps and parameters, awareness models, etc. As used herein“database” means any of a number of different data stores that providesearchable indices for storing, locating and retrieving data, includingwithout limitation, relational databases, associative databases,hierarchical databases, object-oriented databases, network modeldatabases, dictionaries, flat file/XML datastores, flat file systemswith spidering or semantic indexing, and the like. Alternately, or inaddition, the same information can be stored in the memory of thecomputer, which can also be considered a database.

Moreover, such information, including but not limited to the identity ofand information relating to the agents, their parameters, MAPE,optimization steps and parameters, awareness models, etc. can be storedon software stored in the memory and/or the processor. The software maybe able to be read/processed/acted upon by the processor. As usedherein, “software” means one or more computer readable and/or executableinstructions or programs that cause a computer/processor/device toperform functions, actions and/or behave in a desired manner. Theinstructions may be embodied in various forms such as routines,algorithms, modules, methods, threads, and/or programs. Software mayalso be implemented in a variety of executable and/or loadable formsincluding, but not limited to, stand-alone programs, function calls(local and/or remote), servelets, applets, instructions stored in amemory, part of an operating system or browser, bytecode, interpretedscripts and the like. It should be appreciated that the computerreadable and/or executable instructions can be located on one computerand/or distributed between two or more communicating, co-operating,and/or parallel processing computers or the like and thus can be loadedand/or executed in serial, parallel, massively parallel and othermanners. It should also be appreciated that the form of software may bedependent on various factors, such as the requirements of a desiredapplication, the environment in which it runs, and/or the desires of aparticular designer/programmer. The software may be stored on a tangiblemedium, such as memory, on a hard drive, on a compact disc, RAM memory,flash drive, etc., which can exclude signals, such as transitory signalsand/or non-statutory transitory signals.

The computer running the program can also be connected to the internetto receive inputs and provide outputs. The computer can communicate withthe internet or other computers via computer communications. For thepurposes of this application “computer communications” meanscommunication between two or more computers or electronic devices, andcan take the form of, for example, a network transfer, a file transfer,an applet transfer, an email, a hypertext transfer protocol (HTTP)message, a datagram, an object transfer, a binary large object (BLOB)transfer, and so on. Computer communication can occur across a varietyof mediums by a variety of protocols, for example, a wireless system(e.g., IEEE 802.11), an Ethernet system (e.g., IEEE 802.3), a token ringsystem (e.g., IEEE 802.5), a local area network (LAN), a wide areanetwork (WAN), a point-to-point system, a circuit switching system, apacket switching system, and various other systems.

The various functions described above may be provided or contained inits own module. For example, the system may include or utilize a runningmodule for running the agent-based simulation based upon a set ofparameters, an error module for determining an error of a simulation, arevised value module for determining a revised value for one of theparameters of the set of parameters, a re-running module for re-runningthe simulation utilizing the revised value of the one of the parameters,a tracking ratio for tracking a ratio of indicators for each agent, avarying module for varying the ratio of indicators for an agent upon theoccurrence of a triggering event for that agent, a using module forusing the ratio as a factor to model the agent's awareness, a receivingor selecting module for receiving or selecting a first numerical valuefor assigning an attribute of an agent, a random receiving or selectingmodule for receiving or selecting a random numerical value, a firstblending module for blending the first and random numerical values, asecond blending module for blending the intermediate result with thefirst numerical value, or the random numerical value, or with a secondnumerical value, and a utilizing module for utilizing the end value toassign the attribute of the agent in the agent-based model. Each modulecan be a block of software, code, instructions or the like which, whenrun on a computer, provide the desired functions. Each module may beable to interact with the other modules, and may not necessarily bediscrete and separate from the other modules, the ABM system, or othercomponents of the system. The modules in the system may be functionallyand/or physically separated, but can share data, outputs, inputs, or thelike to operate as a single system and provide the functions describedherein.

The outcome/results of the ABM/simulations can be used to determine andimplement marketing strategies and behaviour, or as part of a marketingplan, including drawing up and implementing marketing plans, marking andselling products, displaying advertising, etc.

Although the invention is shown and described with respect to certainembodiments, it should be clear that modifications will occur to thoseskilled in the art upon reading and understanding the specification, andthe present invention includes all such modifications.

What is claimed is:
 1. A method for modeling agent awareness in an agentbased model, the method comprising the steps of: tracking a ratio ofindicators for each agent; varying the ratio of indicators for an agentupon the occurrence of a triggering event for that agent; and using theratio as a factor to model the agent's awareness.
 2. The method of claim1 wherein the triggering event is an advertising event, or a purchasingevent, or a distribution event.
 3. The method of claim 1 wherein thetriggering event is the passage of time.
 4. The method of claim 1wherein the ratio of indicators for each agent relates to at least twotypes of indicators, and wherein the method tracks the number of each ofsaid at least two types of indicators associated with each agent, andwherein each triggering event for an agent causes a predetermined numberof at least one type of indicators to be removed from, or added to, thenumber of that type of indicators associated with the associated agent.5. The method of claim 1 wherein the varying step is repeated for eachof the plurality of agents until beta distribution of ratio ofindicators is reached across the agents.
 6. The method of claim 1wherein the using step includes utilizing the ratio to adjust a purchaseprobability of the agent.
 7. The method of claim 1 wherein themaintaining and varying steps are used to model agent awarenessutilizing the Polya Urn method.
 8. The method of claim 1 furtherincluding the steps of defining a plurality of agents, assigning aplurality of attributes to each agent, wherein one of the attributes isinitial awareness, the method further including running a simulation inwhich each agent is introduced to a stimuli and determining the reactionof each agent to the stimuli.
 9. The method of claim 8 wherein therunning a simulation step is an agent-based simulation which predictsconsumer responses to advertising or promotional efforts.
 10. The methodof claim 8 wherein the awareness attribute models the agent's awarenessof a particular item that is available for purchase.
 11. The method ofclaim 1 wherein the method is implemented on a processor which carriesout the tracking, varying and using steps.
 12. The method of claim 1wherein the agent's awareness takes the form of an awareness frequencyratio representing the percentage of stimuli or other cues which theagent encounters which cause the agent to think about a given brand. 13.A method for running an agent-based simulation comprising: defining aplurality of agents; assigning a plurality of attributes to each agent,wherein one of the attributes is a non-binary awareness attribute thatmodels the agent's awareness of a particular item that is available forpurchase; introducing each agent to a stimulus such that agent reacts tothe stimulus at least partially based upon the agent's assignedattributes; and determining or recording the reaction of each agent. 14.The method of claim 13 wherein the awareness attribute is determined bytracking a ratio of indicators for each agent and varying the ratio ofindicators for an agent upon the occurrence of a triggering event forthat agent.
 15. The method of claim 14 wherein the triggering event isan advertising event, or a purchasing event, or a distribution event, orthe passage of time.
 16. The method of claim 13 wherein the awarenessattribute relates to awareness of a particular brand, and is anunbounded variable that is related to the number of mental associationsthe agent has with the brand.